In this talk, we discuss several control charts for monitoring multivariate processes. These control charts are based on the concept of data depth. We begin with reviewing control charts introduced in Liu (1995). We further construct several new depth based multivariate control charts. Unlike the commonly used multivariate control charts, the charts discussed in this talk are all distribution f...

Modern statistical process monitoring (SPM) applications focus on profile monitoring, i.e., the of quality characteristics that can be modeled as profiles, also known functional data. Despite large interest in literature, there is still a lack software to facilitate its practical application. This article introduces funcharts R package implements recent developments SPM multivariate characteris...

i ABSTRACT There has been much research involving simultaneous monitoring of several correlated quality characteristics that rely on the assumptions of multivariate normality and independence. In real world applications, these assumptions are not always met, particularly when small counts are of interest. In general, the use of normal approximation to the Poisson distribution seems to be justif...

Submitted by Sylvain Verron on Mon, 06/13/2016 13:01 Titre Multivariate control charts with a bayesian network Type de publication Communication Type Communication avec actes dans un congrès Année 2007 Langue Anglais Date du colloque 9-12/05/2007 Titre du colloque 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO'07) Titre des actes ou de la revue Informati...

Control charts have been used effectively for years to monitor processes and detect abnormal behaviors. However, most control charts require a specific distribution to establish their control limits. The bootstrap method is a nonparametric technique that does not rely on the assumption of a parametric distribution of the observed data. Although the bootstrap technique has been used to develop u...